Secondary research may be internal, published publicly, or available via syndicated sources. Secondary data might include background on the market, industry, competitors, and the brand’s history. Primary can help marketers to understand consumers in the market, including psychological makeup, spending and media consumption patterns, and responsiveness to message appeals and offers.
Netnography is an unobtrusive approach to research with a key benefit of observing what is likely to be credible information, unaffected by the research process. Many marketers already use a very informal and unsystematic form of netnography by simply exploring relevant online communities. However, to minimize the limitations of netnography, researchers should be careful in their evaluations by employing triangulation to confirm findings whenever possible.
Step 1: Fetch, crawl, and cleanse. Data from the sources are collected using web crawlers. These are the same types of programs search engines use to catalog web pages. Using the word-phrase dictionary, the crawlers select only the content that appears to be relevant based on matches with the dictionary. This process is called fetching or web scraping. The scraped data need to be cleansed to eliminate unnecessary formatting prior to moving forward. A text classifier (from the dictionary) is then applied to the data to filter any irrelevant content that made it into the data set. Step 2: Extract entities of interest. From this filtered set of content, relevant posts are extracted. The data are filtered again using rules to tag the entities of interest and further narrow the data set. Step 3: Extract sentiment. From there, the analyst can begin sentiment extraction using sentiment indicators. These are words or other cues used to indicate positive or negative sentiment. A sentiment dictionary specifies sentiment indicators and rules to be used in the analysis. Step 4: Aggregate raw sentiment data into a summary. Raw sentiments are then aggregated creating a sentiment summary.
• First and foremost is accuracy in gauging sentiment with automated tools. The sheer volume of conversation creates an information overload issue for most brands wanting to use social media monitoring and research. The solution is the use of an automated system, but these systems still struggle with accuracy in the coding of meaning. • Cultural factors, linguistic nuances, and differing contexts all make it difficult to code text into negative, neutral, or positive categories. Consider this example: A search on attitudes toward the movie Julie & Julia revealed a positive sentiment score from 77 percent of tweets related to the movie. But some tweets may have been miscoded. A tweet reading “Julie and Julia was truly delightful! We all felt hungry afterwards” was coded as negative. The word “hungry” was used as a sentiment indicator for negative. A person could understand that this statement was positive for the movie, but the software program couldn’t. Linguistic nuances make it difficult for mining software to achieve better accuracy levels. A chocolate torte described as wickedly sinful would be coded as negative, when it fact the descriptor is positive. • Defining the sentiment dictionary can also be a challenge, ultimately affecting whether the right words are extracted. Words can have many meanings. Take BP, for instance. As the oil spill in the Gulf of Mexico has created a public relations crisis for the company, measuring sentiment before and after recovery steps and announcements is a useful tool for gauging damage control for the brand’s image. But in a world of acronyms, BP may mean blood pressure, border patrol, business plan, Brad Pitt, or bipolar disorder. • Accuracy in the categorical data needed to make better use of data is also an issue. It’s difficult to gauge who is making comments (which segments they represent) in terms of demographic and geographic descriptors. Conversation origin may be identifiable using the URL, the IP address, or the language used, but all of these methods have flaws. The URL and IP address are not always helpful (take Facebook, for instance, with users around the world). Language indicators likewise leave a lot to be desired.
To conduct a content analysis, the text is coded, or broken down, into manageable categories on a variety of levels—word, word sense, phrase, sentence, and theme—and then examined further for interpretation. Using codes, labels that classify and assign meanings to pieces of information, analysts can use the comments to determine any themes that are reflected in the comments.
Engagement is a complex construct made up of several individual accomplishments. The Engagement Food Chain illustrates the hierarchy of effects we seek from our target audience as the reach increasing levels of engagement with our brand. Figure 10.1 demonstrates how we look for different outcomes depending upon the consumer’s level of engagement with the brand.
When it comes to social media marketing—or any form of marketing, for that matter—measurement isn’t optional. It’s a necessity for organizations that are serious about adjusting their strategies and tactics to better meet their objectives. Some may feel intimidated about specifying what it is they want to see happen when it comes to their social media activities; perhaps they believe this sets them up to fail because they’re not sure they can actually define or attain specific goalposts. Others may still be in the early stage of the social media maturity life cycle discussed in Chapter 2; because they’re still “playing” with social media, they don’t yet feel the need to define what results they would like to see. But ultimately social media will have to answer to the same masters as other kinds of traditional media—the bean counters that need to see value for their money. The investment in social media marketing will require justification. Strategists will want to understand what’s working and what isn’t in order to decide if a campaign needs fixing or if it’s worth continuing at all.
1. MKT 380
Week 10 Introduction to Social Media Marketing
2. Chapter Objectives
• Describe the types of data used in social media
• Discuss the methods and usages of quantitative and
qualitative social media research
• Discuss the functions of metrics in social media
• Assess the costs and benefits of social media
• Describe methods to track social media marketing
3. The Role of Social Media in Research
Marketers rely on several variants of marketing research
to make decisions. Options include:
• Secondary research – information already collected
and available for use.
• Primary research – information collected solely for the
research purpose at hand.
• In Social media the presence
of so much information
has resulted in the idea of
4. Qualitative Social Media
involves recording behavior
or the residual evidence of
5. Qualitative Social Media
Ethnographic Research occurs
when marketing researchers
conduct field research by visiting
people’s homes and offices to
observe them as they go about
Netnography is a rapidly growing
research methodology that adapts
ethnographic research techniques
to study the communities that
emerge through computermediated communications.
6. Quantitative Social Media
Monitoring and Tracking
• Social media monitoring occurs by carefully choosing and
searching the appropriate key words and the relevant social
• This process answers four basic questions:
How many times was the search term found?
When was the search term found?
Where was the search term found?
Who mentioned the search term?
7. Quantitative Social Media
Sentiment Analysis refers to determining
how people think or feel about an object.
The analysis consists of four steps:
Fetch, crawl and cleanse.
Extract entities of interest.
Aggregate raw data into a summary.
8. Quantitative Social Media
The challenges of sentiment analysis
Accuracy in gauging sentiment with automated tools
Cultural factors, linguistic nuances and differing contexts
Defining the sentiment dictionary
Accuracy in the categorical data needed to make better use of data
9. Quantitative Social Media
Content Analysis is an
used to identify the
presence of concepts
and themes within
qualitative data sets.
Click here to read more
about Dove’s use of
social media mining
10. Social Media Metrics
11. What Matters is
In many ways, social media marketing mimics online
advertising in terms of the viable metrics available to
measure how effective these messages are. Advertisers
• Reach - the number of people exposed to the message
• Frequency - the average number of times someone is exposed
• Site stickiness - the ability of a site to draw repeat visits and to
keep people on a site
• Relative pull - a comparison of how well different creative
executions generate a response of creative advertising
• Clickthroughs - the number of people exposed to an online ad or
link who actually click on it
• Sales conversions - the number of people who click through who
go on to purchase the product
• Viewthroughs - the number of people who are exposed and do
not click through, but who later visit the brand’s website
12. What Matters is
A First Date or a Marriage?
• Simply counting the quantity of interactions consumers have with a
brand doesn’t tell us much about the quality of these touchpoints.
We need to know the degree of engagement people feel during
and after the interaction, and how these exposures influenced their
feelings about the brand.
• Key performance indicators (KPIs) are those metrics that are
tied to organizational objectives.
13. What Matters is
• Measurements within a defined context are metrics.
• Measurements require context to provide useful
• Metrics that are tied to objectives are key performance
• Objectives must be well-defined before we can identify
key performance indicators.
14. The Evaluation and
The measurement plan is organized according to a four-step
process known as the DATA approach.
1. Define: Define the results that the program is designed to
2. Assess: Assess the costs of the program and the potential value
of the results.
3. Track: Track the actual results and link those results to the
4. Adjust: Adjust the program based on results to optimize future
15. The Evaluation and
Simple Ways to Start Measuring
• C ontent consumption: Who is interacting with and consuming the
brand-generated and consumer-generated content?
• C ontent aug mentation: Who is adding to or changing your content by
continuing the conversation with response posts?
• C ontent sharing : At what rate are those exposed to the brand messages
sharing the content with others using Share tools?
• C ontent loyalty: How many consumers have subscribed to branded
content with RSS feeds or by registering for site access?
• C ontent conversations: Who is discussing the brand?
• C ontent eng ag ement: Is the number of friends to brand profiles